class_docstr: Apply a random transformation to the brightness of a tensor image.
  
  This implementation aligns PIL. Hence, the output is close to TorchVision.
  
  .. image:: _static/img/RandomBrightness.png
  
  Args:
      p: probability of applying the transformation.
      brightness: the brightness factor to apply
      clip_output: if true clip output
      silence_instantiation_warning: if True, silence the warning at instantiation.
      same_on_batch: apply the same transformation across the batch.
      keepdim: whether to keep the output shape the same as input (True) or broadcast it
               to the batch form (False).
  Shape:
      - Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`, Optional: :math:`(B, 3, 3)`
      - Output: :math:`(B, C, H, W)`
  
  .. note::
      This function internally uses :func:`kornia.enhance.adjust_brightness`
  
  Examples:
      >>> rng = torch.manual_seed(0)
      >>> inputs = torch.rand(1, 3, 3, 3)
      >>> aug = RandomBrightness(brightness = (0.5,2.),p=1.)
      >>> aug(inputs)
      tensor([[[[0.0505, 0.3225, 0.0000],
                [0.0000, 0.0000, 0.1883],
                [0.0443, 0.4507, 0.0099]],
      <BLANKLINE>
               [[0.1866, 0.0000, 0.0000],
                [0.0000, 0.0000, 0.0000],
                [0.0728, 0.2519, 0.3543]],
      <BLANKLINE>
               [[0.0000, 0.0000, 0.2359],
                [0.4694, 0.0000, 0.4284],
                [0.0000, 0.1072, 0.5070]]]])
  
  To apply the exact augmenation again, you may take the advantage of the previous parameter state:
  
      >>> input = torch.rand(1, 3, 32, 32)
      >>> aug = RandomBrightness((0.8,1.2), p=1.)
      >>> (aug(input) == aug(input, params=aug._params)).all()
      tensor(True)
code: def apply_transform(
      self,
      input: Tensor,
      params: Dict[str, Tensor],
      flags: Dict[str, Any],
      transform: Optional[Tensor] = None,
  ) -> Tensor:
      brightness_factor = params["brightness_factor"].to(input)
      return adjust_brightness(
          input, brightness_factor - 1, self.clip_output
      )
  
file_path: kornia/kornia/augmentation/_2d/intensity/brightness.py
imports: from typing import Any, Dict, Optional, Tuple
  from kornia.augmentation import random_generator
  from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
  from kornia.augmentation.utils import _range_bound
  from kornia.core import Tensor
  from kornia.enhance.adjust import adjust_brightness
label_desc: Use image node_id 2 for calling the RandomBrightness obj's underlying member method code with example usage: obj.apply_transform(input, params, flags, transform) and returns: adjust_brightness